According to [ 1 ], Kenya has one of the largest HIV epidemic in the world with a prevalence rate of 5. Combating the epidemic requires strategies that reduce the new infections and improvement of the survival rates of those already infected. The benefits of combined antiretroviral therapy are well documented in literature [ 3 , 4 ].
After initiation of antiretroviral therapy ART most patients experience a reduction in HIV viral load combined with an increase in CD4 cell count which reduces the risk of HIV related events and death. Changes in CD4 count constitute an important component in patient monitoring and evaluation of treatment response as these patients do not have access to routine viral load testing.
WHO recommends CD4 count monitoring every 6 months and viral load testing only when the capacity exists [ 6 ]. However, the measurements of CD4 in most developing countries is not on a regular basis. Revision of WHO guidelines in brought changes in the management of HIV infected patients among them use of less toxic antiretroviral drugs in first line [ 2 ].
In particular the recommendation emphasized moving away from the use of Stavudine d4T [ 7 ]. The success of HAART nonetheless critically depends on regular patient follow-up to the treatment during their lifetime. It is important to maintain the patients on first-line treatment regimens as much as possible since a second-line treatments are expensive [ 9 ].
ART drug regimens are however changed due to various reasons which include but not limited to toxicity, co-morbidity, pregnancy and treatment failure [ 10 , 11 ]. These drug regimen modifications limit treatment options and introduce challenges such as monitoring and adherence difficulties among the patients.
These modifications have also been associated with poor clinical outcomes [ 12 ]. According to [ 13 ], fewer drug substitutions may be important for success and control cost of managing HIV patients from a clinical stand point.
It is therefore of utmost importance to understand when ART regimens are switched in order to identify drugs that can be administered in real world settings and assess factors that are associated with these treatment modifications.
In this study we aim to assess CD4 cell count trends over time for patients on combined ART in one of the comprehensive health care clinics in Nairobi, Kenya and evaluate the rate of change in CD4 count in response to antiretroviral treatment as an indicator of how fast the patients responded to treatment. Further, we estimate time to first drug regimen change and establish if it is associated with the baseline characteristics of the patients.
Minimal treatment switches would be an indicator of less adverse treatment effects. In " Methods " section we discuss the methods used, the results are then displayed in " Results " section and finally a comprehensive discussion and conclusion is presented in " Discussion " section. Informed consent was obtained from all participants included in the study. Baseline characteristics of the patients at initiation of ART such as patient gender, age and WHO clinical stage are also included.
All methods were performed in accordance with the relevant guidelines and regulations. In this study we excluded any patients who were on d4T as it was being phased out. During the follow up period some of the patients changed regimens for different reasons. Whilst the number of changes made by a patient may be more than one,in this study we considered the first regimen change as our outcome of interest.
The time to first treatment change was calculated by subtracting the date of ART initiation from the date of ART modification. Exploratory data analysis and descriptive statistics was carried out to give an insight into the data. Baseline categorical variables were cross tabulated to give the proportions in different categories and a Chi-square test performed to find out if any association existed among the variables.
Patients were censored if treatment change was not observed until their last visit to the clinic. This was done for patients who were lost to follow up and for those still alive at the end of study.
Time to treatment change was estimated using the Kaplan-Meier estimator. The log-rank LR test was used to compare between groups of baseline characteristics and initial treatment administered to the patients. The Cox regression model [ 15 ] was used to identify the baseline characteristics that could be associated with first treatment or regimen modification. Details of the Cox regression model are in Additional file 1. CD4 cell counts were log-transformed to meet the assumption about stability of the variance with increasing CD4 cell count.
We constructed a mean profile of the log transformed CD4 over time in months. Further, the average profile plots were fitted for different baseline characteristics. A simple parametric model may be adequate to describe subject-specific profiles in terms of random effects [ 16 ]. However, the relevance of normality assumption on random effects may be questionable.
Furthermore, the individual profiles are non-linear making parametric models too restrictive [ 17 ]. We propose a data-driven approach based on semi-parametric regression models [ 18 , 19 ]. In this approach, a patient specific random intercept is used to capture the correlation of CD4 cell count measurements over time within the patients.
We assume patient specific random parameters for both linear and quadratic time effects to capture different evolution patterns between the patients. This model allows smoothing with respect to time. The link between mixed model and smoothing provides a flexible framework for estimating profiles in a data driven way [ 20 ].
The rate of change is helpful in assessing the effectiveness of treatment administered to the patients. A more detailed model formulation is attached in the supporting materials Additional file 1. The proposed model was fitted using the gamm procedure available in R package mgcv [ 21 ]. A total of patients with at least two measurements of CD4 count were included in the study. Table 1 provides a summary of the CD4 count measurements and age of the patients.
Subjects were followed up for a maximum of days. The median time of follow up was green days. The number of CD4 count measurements per subject ranged from two to fifteen with a median of 6 measurements. A majority of the patients These average CD4 cont values fluctuated over time as shown in Table 5. The highest CD4 count being with a median value of Categorical baseline characteristics are summarized in Table 2.
Most patients in the study were females at At initiation of ART A sample of twenty individual profiles of the patients are presented in Fig. From the profiles we observe that there is within and between subject variability.
The subjects start at different baseline CD4 counts and evolve differently over time. There is an indication that the overall trend is not linear over time. The number of patients that had at least one treatment change account for about 7. The Log-rank test was used to test the difference between categories of baseline covariates with the probability of treatment modification.
There was no significant difference for WHO clinical stage. The Kaplan—Meir curves are shown in Fig. The survival curve for time to treatment change shows steady increase on overall. The estimated median time to first treatment change was not reached since there were few regimen changes. The average time to treatment change was estimated to be days. Kaplan—Meir curves for time to treatment change by different baseline characteristics. The cox regression analysis results are presented in Table 4.
Patients initiated on Zidovudine were at a higher risk of changing treatment compared to those on Tenofovir. The unadjusted analysis show that males were less likely to have treatment modification compared to females.
This result was however only observed in the unadjusted model and was not in the adjusted analysis. Graph of logarithm CD4 count over time in days. At later time points we observe that the logarthim CD4 cell count of those taking EFavirenz is higher than for those on Nevirapine.
The fitted individual profiles for the patients and overall average trend of logarithm CD4 count from semiparametric model is shown in Fig. There was a rise in the logarithm CD4 cell count in the first days after initiation of ART and thereafter it stabilizes. Corresponding figures for were 24, participants, Notes: Data are unweighted. Percentages are calculated with respect to previous row, except interview response rates shown in first row which are calculated with respect to total eligible population not shown.
For females, it increased from The unadjusted proportion on treatment also increased significantly for the Rift Valley region, for those aged 35—49 years, for persons who were married or cohabitating, and for those who disclosed their status to their most recent sex partner.
However, the likelihood of being on ART differed by age and marital status in both surveys. Women who were pregnant in the last three years were less likely to be on ART in both surveys Table 1. In , when controlling for residency, age group, disclosure, and recreational drug use, ART non-use was more likely in respondents living in rural compared with urban areas aOR 1.
ART non-use was also more likely in those who reported they had not disclosed their status to their last sex partner aOR 2. In order to explore the association between recent pregnancy and ART non-use among women, separate multiple regression models were constructed for women in both and No significant association between lack of ART use among those recently pregnant was found in aOR 1.
However, the women-only model for did reveal associations with marital status, education, and wealth quintile not seen in the combined male-female model. Unlike in the combined male-female model, disclosure of status and recreational drug use in last 12 months was not found to be associated with ART non-use in women S5 Table. ART use increased more than four-fold among those living in rural informal areas from 7.
Although ART use increased across all age groups, increases were much greater among the older age groups with On bivariate analysis, ART non-use was significantly associated with province, marital status, highest level of education, employment status and household wealth in Recency of testing showed no significant association with ART non-use in either survey year S4 Table.
On multiple regression analysis, marital status was significantly associated ART non-use: being divorced, separated or widowed was associated with a lower likelihood of ART non-use when compared to being single or never married aOR 0. Significant associations were also observed by level of education aOR 0. Use of ART increased significantly in both countries, consistent with ART program scale-up and evolving treatment guidelines in both Kenya and South Africa during the study period.
While in South Africa it appears disparities in ART use may have decreased over the period of analysis, in Kenya household wealth among women and rural residency among men and women combined were both significantly associated with ART non-use in but not in In both countries, after controlling for other covariates, there were no significant disparities in ART use by province and sex.
In Kenya in lack of ART use was higher in rural areas while no differences were observed by locality type in South Africa in either survey year. Differences by household wealth were only seen in in Kenya, among women only, and in in South Africa, where they were limited to one quintile.
Older age was associated with greater likelihood of ART use in both countries in both survey periods when controlling for other factors. This may partially reflect natural disease progression and ART eligibility criteria at the time, with older people more likely to have been infected for a longer period of time compared to younger people, thus reaching the thresholds for ART treatment required by contemporary treatment guidelines. Nonetheless this finding of age differences in non-use of ART among PLHIV suggests need for strengthening efforts to ensure that younger people know their status, start ART, and adhere to care and treatment.
With the adoption of the test-and-treat policy in in both countries, decreasing disparities in ART use by age group are expected in the future. Nonetheless, high uptake of testing among young people combined with appropriate linkage to care will be a necessary precondition to achieving adequate ART coverage.
In South Africa this policy was implemented in January [ 27 ]. Overall, household wealth and educational level were not associated with ART use in either country in , which is perhaps a result of the large, government-sponsored ART programs in both Kenya and South Africa offering ART to all regardless of ability to pay.
However, in a sub-group analysis among women in Kenya, ART use was associated with increasing household wealth among women. Point estimates for ART use increased across all regions except Nyanza, though only Rift Valley increased significantly. The appearance of reduced ART use in rural relative to urban areas in , not seen in , may reflect either reduced barriers to access in urban areas or challenges in accessing drugs in rural areas due to increased travel costs, fear of stigma, or other barriers, and is a cause for concern [ 29 , 30 ].
In South Africa, HIV prevalence also varied across provinces and geographic locations, being highest in both urban and rural informal settlements in While ART use was lowest in rural informal settlements in , it increased dramatically in reaching the same levels of use observed in other locality types, suggesting success in increasing geographical equity in ART access in South Africa over time.
This suggestion is further corroborated by the absence of significant associations between ART non-use and province in both survey years and by locality type in Comparatively lower ART use among South African students in , even when controlling for age, is an interesting finding, and could also be a reflection of the healthy worker effect as well as the natural disease progression as discussed above.
This analysis was subject to several limitations. Antiretroviral treatment access from — in both countries was highly dependent on treatment guidelines implemented at the time that individual patients engaged in care, and these guidelines have evolved over time. Our analysis did not attempt to establish eligibility for treatment, nor would it be entirely possible in a cross-sectional study to account for eligibility prior to ART initiation, but rather included all PLHIV regardless of eligibility.
Data regarding distance to the nearest facility offering ART, which may have provided further insights about ART accessibility, were not available. Finally, sample size was limited for some domains, especially in Kenya, which could have limited our power to detect differences in some cases and prevented more detailed sex-specific analyses.
In spite of these limitations, measurement of ART use in nationally-representative surveys in two countries with large HIV treatment programs allowed for an objective assessment of trends in ART use among PLHIV in the era of treatment expansion and identification of potential barriers to ART access and uptake.
Both countries demonstrated substantial increases in ART use over time with broadly equitable access to ART across geographic and socioeconomic status with some exceptions. Our analyses did find evidence of increasing disparities in access to ART by household wealth in women in Kenya, and lingering inequities in use by age and among those who use recreational drugs, indicating some populations may require increased adherence support or access to HIV testing.
To the degree that lower use in youth may be due to eligibility criteria, the adoption of test-and-treat provides a new opportunity to increase access to ART among youth. Authors would like to thank Yajna D.
Moloo for her helpful literature review on socioeconomic factors affecting ART use in sub-Saharan Africa, the University of Cape Town Department of Clinical Pharmacology for conducting the ART biomarker testing, the study teams that conducted the studies in South Africa and Kenya and finally the survey participants.
Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Conclusion Although we found substantial increases in ART use in both countries over time, we identified areas needing improvement including among rural Kenyans, students in South Africa, and among young people and drug users in both countries.
Competing interests: The authors have declared no competing interests exist. Data analysis Data from each survey were analysed separately. Statistical methods Logistic regression analysis was performed to assess associations between lack of ART no evidence of ART exposure in the blood specimen and selected demographic, socioeconomic and behavioural variables separately for each survey.
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BMC Infect Dis. Download references. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the funding agencies. You can also search for this author in PubMed Google Scholar. All authors read and approved the final manuscript. Correspondence to Tom Oluoch. This study is a secondary analysis of de-identified data and IRBs approved it with no requirement for additional consent from the participants.
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Results Among eligible patients receiving ART, Methods We conducted a prospective, cluster randomized controlled study in Siaya County, western Kenya to assess the effect of an EHR with CDSS compared to EHR only on timely identification of patients experiencing immunological treatment failure and appropriate action taken [ 17 ]. Setting and patient population Siaya county, where the study was conducted, has one of the highest HIV prevalence in Kenya. Randomization Of the 20 facilities where KEMRI provides data management support, seven were excluded from the study as they did not have reliable electric power, a secure location for a computer, or permanent data clerks to help with the regular data management activities.
Outcome measures The primary outcome measure for this study was the proportion of patients receiving HIV treatment that were LTFU at least once during the study period. Statistical analysis The sample size calculation was adapted from the method used in the main study reported in [ 17 ]. Missing data considerations The data contained missing values for some of the patient-level covariates.
Ethical review The study was reviewed in accordance with the Centers for Disease Control and Prevention CDC human research protection procedures and was determined to be research, but CDC investigators did not interact with human subjects or have access to identifiable data or specimens for research purposes.
This trial is registered with ClinicalTrials. The study profile. Full size image. Time from study enrollment to documentation of first LTFU. References 1. Article Google Scholar 4. Article Google Scholar 5.
Article Google Scholar 6. Article Google Scholar 7. Article Google Scholar 9. Article Google Scholar Article Google Scholar Download references. Disclaimer The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the funding agencies.
View author publications. Ethics declarations Ethics approval and consent to participate The study was reviewed in accordance with the Centers for Disease Control and Prevention CDC human research protection procedures and was determined to be research, but CDC investigators did not interact with human subjects or have access to identifiable data or specimens for research purposes.
Competing interests All authors declare no conflict of interest in conducting the study and preparation of the manuscript for publication. Additional information Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. About this article. Cite this article Oluoch, T. Copy to clipboard.
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